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Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. AI analytics may misinterpret activity when it lacks contextual understanding, producing false positives or missing meaningful risks. This can directly undermine continuous monitoring decisions, making contextual model error more significant than cost or legacy integration issues. This makes option B, Model errors caused by inability to understand context, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk- management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
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